Development and Internal Evaluation of AI-Assisted Cervical Muscle-Based Scores (FUNC-RISK) in Head and Neck Cancer:...

An accurate prognostic assessment is essential to optimize treatment strategies in head and neck cancer (HNC). This study aimed to develop and internally evaluate an AI-assisted survival risk score derived from automatically quantified cervical muscle parameters on routine radiotherapy-planning CT scans.

Pretreatment CT images were processed in a single-center cohort of 65 HNC patients, using AI-assisted automated segmentation to obtain the cervical skeletal muscle index (SMI), intramuscular adipose tissue area (IMAT), and mean muscle attenuation (HU). A multivariable Cox regression model was used to generate the continuous FUNC-RISK score, and model performance was assessed using time-dependent ROC curves at 36 and 60 months.

Patient-, tumor-, and treatment-related characteristics were not predictive of survival. SMI (p = 0.006) and IMAT (p = 0.047) were significantly associated with overall survival in a univariable analysis, while HU showed a borderline association (p = 0.087). All three parameters were included in the multivariable model, yielding the following equation: FUNC-RISK = (-0.364 × SMI) + (-0.087 × IMAT) + (0.011 × HU). The model demonstrated moderate discrimination (AUC = 0.734 at 36 months; 95% CI 0.604-0.863; p = 0.002, and AUC = 0.689 at 60 months; 95% CI 0.558-0.819; p = 0.009). Based on the median score (-3.18), patients were stratified into low- and high-risk groups. Five-year overall survival was 71.9% ± 7.9% for the low-risk group versus 39.4% ± 8.5% for the high-risk group (p = 0.006).

FUNC-RISK provides preliminary evidence of clinically meaningful prognostic stratification based on AI-derived cervical muscle quantity and quality metrics obtained from routine radiotherapy-planning CT scans. These exploratory results support the potential role of automated body-composition analysis in personalized risk assessment for HNC, although external multicenter validation is required before clinical implementation.